Abstract
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.
Original language | English (US) |
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Pages (from-to) | 88-106 |
Number of pages | 19 |
Journal | Statistical Science |
Volume | 25 |
Issue number | 1 |
DOIs | |
State | Published - 2010 |
Externally published | Yes |
Keywords
- Mixture kalman filter
- Parameter learning
- Particle learning
- Sequential inference
- Smoothing
- State filtering
- State space models
ASJC Scopus subject areas
- Statistics and Probability
- General Mathematics
- Statistics, Probability and Uncertainty